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MEng e-voting project published in a journal paper

As part of a 2021/2022 MEng group project, Horia Druliac, Matthew Bardsley, Chris Riches, and Christian Dunn implemented a fully functional end-to-end (E2E) verifiable online voting system and conducted a successful trial among the residents of New Town in Kolkata, India during the 2022 Durga Puja festival celebration. This was the first time an E2E online voting system was built and tested in India. The feedback was overwhelmingly positive. Full details about the implementation, the trial and the voter feedback are written in a paper, published in the Journal of Information Security and Application. A free version of the paper is available on IACR e-print as a technical report. Also, see the earlier news item about this Durga Puja trial.

Professor Feng Hao, who supervised this group project, commented: “This is great teamwork. The four MEng students worked relentlessly for nearly a year, with good assistance from Luke Harrison and Professor Bimal Roy. The e-voting system was developed at an industry standard and worked flawlessly during the Durga Puja trial. Several government officials from India also helped us, providing invaluable support for the trial. We sincerely thank them in the acknowledgement section of the paper.”

Latest academic promotions

We are happy to announce four promotions in the department:

Many congratulations to our colleagues for all their achievements!

Seven papers accepted to NeurIPS 2023

Seven papers authored by Computer Science researchers from Warwick have been accepted for publication at the 37th Conference on Neural Information Processing Systems, the leading international venue for machine learning research, which will be held on 10-16 December 2023 in New Orleans, Louisiana, USA:

  • EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event Cameras, by Guangrong Zhao, Yurun Yang, Jingwei Liu, Ning Chen, Yiran Shen, Hongkai Wen, and Guohao Lan
  • Fully Dynamic k-Clustering in Õ(k) Update Time, by Sayan Bhattacharya, Martin Costa, Silvio Lattanzi, and Nikos Parotsidis
  • Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks, by Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, and Volkan Cevher
  • Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs, by Dmitry Chistikov, Matthias Englert, and Ranko Lazic
  • On the Convergence of Shallow Transformers, by Yongtao Wu, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher
  • Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask? by Hoang Pham, The Anh Ta, Shiwei Liu, Lichuan Xiang, Dung Le, Hongkai Wen, and Long Tran-Thanh
  • Towards Unbounded Machine Unlearning, by Meghdad Kurmanji, Peter Triantafillou, and Eleni Triantafillou

Faculty PhD Thesis Prize Awarded to Teddy Cunningham

We are pleased to announce that Dr Teddy Cunningham has been awarded a Faculty of Science, Engineering, and Medicine (SEM) PhD Thesis Prize. Each year, the SEM Faculty funds a prize for the best PhD/EngD thesis entered into the competition. Each department nominates a winner out of the applications received after a judging process as determined by the Faculty.

Teddy’s thesis is titled “Generating and Sharing Differentially Private Spatio-Temporal Data Using Real-World Knowledge”, and was supervised by Prof Hakan Ferhatosmanoglu. The thesis includes solutions for sharing trajectory data using local differential privacy, and incorporating constraints and relationships of data records into differential privacy that improves their utility while preserving the theoretical privacy guarantees. An example application is using road network information for improving the quality of privately shared location datasets.

New spin-out to make e-voting more secure, accessible and trustworthy

Researchers from the Systems and Security theme, Department of Computer Science have created a new spin-out company, SEEV Technologies Ltd, to build end-to-end (E2E) verifiable e-voting systems for future elections. An E2E verifiable voting system allows every voter to verify that their vote is properly cast-as-intended, recorded-as-cast and tallied-as-recorded while preserving the voter's privacy. SEEV (self-enforcing e-voting) is a new paradigm of E2E voting technology that enables voters to fully verify the tallying integrity of an election without needing any trustworthy tallying authority, hence the system is "self-enforcing".

This joint spin-out from the University of Warwick and Newcastle University is built on an ERC-funded starting grant ("Self-Enforcing E-Voting System: Trustworthy Election in Presence of Corrupt Authorities", No. 306994, PI: Professor Feng Hao) initially hosted at Newcastle University and later transferred to the University of Warwick. The company is co-founded by Professor Feng Hao and Dr Siamak Shandahshti (co-inventors), and led by Dr Stewart Hefferman (CEO). SEEV has been prototyped and successfully tested in several trials in the past, supported by an ERC Proof of Concept grant (No. 677124), a Royal Society International collaboration award (CA\R1\180226), and an Innovate UK Cybersecurity Academic Startup Accelerator Programme (CASAP). SEEV Technologies Ltd has received seed funding from Oxford-based Global Initiative to build SEEV systems for real-world elections.

A University of Warwick press release is here.

Mon 24 Jul 2023, 15:03 | Tags: Research Data Science Systems and Security

Latest two academic promotions

We are happy to announce that Dr Gihan Mudalige and Dr Victor Sanchez have both been promoted to Professor from 1st August 2023.

Many congratulations to our colleagues for all their achievements!

Spying on the Spy: Security Analysis of Hidden Cameras

When you purchase an IP-based spy (hidden) camera for surveillance, are you aware that others may be spying on what you are watching? Recent research by Samuel Herodotou in the Department of Computer Science, Warwick, as part of his third-year undergraduate dissertation project under the supervision of Professor Feng Hao, has revealed a wide range of vulnerabilities of a generic camera module that has been used in many best-selling hidden cameras. Exploiting these vulnerabilities, an attacker may capture your hidden camera's video/audio streams from anywhere in the world, and furthermore, take complete control of the camera as a bot to attack other devices in your home network. To launch the attack, all the attacker needs to know is merely your hidden camera’s serial number. It is estimated that these vulnerabilities affect millions of hidden cameras, mostly sold in America, Europe and Asia. The (insecure) peer-to-peer network that is used by the affected cameras is also being used by 50 million IoT devices as a general communication platform. Hence, many millions of other IoT devices may also be affected. Researchers have responsibly disclosed findings to the manufacturers, and a CVE has already been assigned. Samuel will present this research work at the 17th International Conference on Network and System Security (Canterbury, UK, 14-16 August 2023). More details can be found in the paper.

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